Next Article in Journal
Progress and Limitations in Forest Carbon Stock Estimation Using Remote Sensing Technologies: A Comprehensive Review
Previous Article in Journal
The Influence of Climate and Hydrological Factors on the Phenological Characteristics of Populus euphratica in the Oasis of the Central Taklamakan Desert
 
 
Search for Articles:
Title / Keyword
Author / Affiliation / Email
Journal
Article Type
 
 
Advanced Search
 
Section
Special Issue
Volume
Issue
Number
Page
 
Journals
Forests
Volume 16
Issue 3
10.3390/f16030448
forests-logo
Submit to this Journal Review for this Journal Propose a Special Issue
► Article Menu

Article Menu

share Share announcement Help format_quote Cite
first_page
settings
Order Article Reprints
Font Type:
Arial Georgia Verdana
Font Size:
Aa Aa Aa
Line Spacing:
Column Width:
Background:
Article

Study on the Distribution Range and Influencing Factors of Salix oritrepha Schneid. and Picea crassifolia Kom. in the Watershed of the Yellow River Under Future Climate Models

by
Shengqi Jian
1,
Lilin Kong
1,
Shentang Dou
2,3,*,
Yufei Han
1 and
Jiayi Wang
2,3
1
College of Water Conservancy and Transportation, Zhengzhou University, Zhengzhou 450001, China
2
Key Laboratory of Yellow River Channel and Estuary Regulation of the Ministry of Water Resources, Yellow River Institute of Hydraulic Research, Zhengzhou 450003, China
3
Yellow River Laboratory, Zhengzhou 450003, China
*
Author to whom correspondence should be addressed.
Forests 2025, 16(3), 448; https://doi.org/10.3390/f16030448 (registering DOI)
Submission received: 17 January 2025 / Revised: 19 February 2025 / Accepted: 24 February 2025 / Published: 2 March 2025
(This article belongs to the Section Forest Ecology and Management)
Browse Figures
Versions Notes

Abstract

:
The watershed of the Yellow River is an important water conservation area in the Yellow River Basin. Its fragile ecological environment, climate change and unreasonable human activities have led to the continuous degradation of plant community structure in the watershed. This study only considers environmental factors, based on MaxEnt, Garp and other niche models and spatial-temporal analysis methods such as Mess and MoD analysis, to explore the suitable areas of Salix oritrepha Schneid. (First published in C.S.Sargent, Pl. Wilson. 3: 113 (1916)) and Picea crassifolia Kom. (First published in Bot. Mater. Gerb. Glavn. Bot. Sada R.S.F.S.R. 4: 177 (1923)) in the watershed of the Yellow River under different emission scenarios in the future. The results show that the MaxEnt model has a good simulation effect. In terms of spatial distribution, the suitable areas of the two species are mainly concentrated in the southeastern part of the Yellow River source area. Compared with the current period (1970–2000), by 2070, the suitable areas of the two species in each scenario showed a distribution of high in the east and low in the west, with an obvious expansion trend in the area and moving to high altitude and high latitude. According to the analysis of Mess and MoD, the annual average temperature (Bio_1) may be the most important variable affecting the future distribution of the two vegetation types.

1. Introduction

The sixth assessment report of the United Nations Intergovernmental Panel on Climate Change (IPCC) shows that the global surface average temperature in 2011–2020 is about 1.09 °C higher than that in 1850–1900 [1]. The temperature increase caused by climate change has an important impact on terrestrial ecosystems, which will directly or indirectly alter the spatial distribution pattern of species that inhabit fragile ecosystems. Therefore, in the context of future global climate change, it is necessary to adopt appropriate response strategies to mitigate the impact of climate change on ecosystems.
Affected by global warming and human activities, the Qinghai–Tibet Plateau, known as the third pole of the Earth, the watershed of the Yellow River in the northeast of the plateau, is facing a series of ecological and environmental problems and has become one of the key areas for global climate change research in the past 40 years [2,3]. As the impact of natural and anthropogenic factors on the environment of the Yellow River Basin continues to increase, significant changes in the ecological environment of this region are expected in the future, which will in turn affect the stability of the entire ecosystem. Vegetation plays a crucial role in the hydrological cycle through processes such as water absorption by its roots and interception by its canopy. Therefore, studying the future changes in vegetation in the watershed of the Yellow River has important reference value for formulating the ecological protection strategy of the basin and ensuring the future high-quality development of the basin.
Salix oritrepha Schneid., as a keystone ecological tree species in the watershed of the Yellow River, has the advantages of wide distribution and high productivity [4,5]. It plays an important role in providing habitat for wildlife and maintaining community structure in the watershed of the Yellow River. The shrubland areas with it as the dominant species have made a significant contribution to the stability of local animal husbandry and the regional ecosystem, for example, providing ecological shelter and improving the soil environment [6,7]. Picea crassifolia Kom. is a unique tree species in the northeastern region of the Qinghai–Tibet Plateau, which is widely distributed at altitudes of 1600–3800 m. P. crassifolia is an important commercial timber because of its tall, straight bole, soft wood and linear texture. At the same time, due to its good drought resistance and rapid growth rate, P. crassifolia is considered to be an excellent afforestation tree species in eastern Qinghai and the watershed of the Yellow River. It plays an important role in maintaining ecological balance, soil and water conservation, water conservation and biodiversity in the watershed of the Yellow River [8,9,10]. As an important dominant species of trees and shrubs in the watershed of the Yellow River, it is of great reference value to study the variation in potential suitable areas under the background of future climate change for revealing the overall migration of vegetation in the watershed of the Yellow River.
In recent years, the relationship between plant geographical distribution and climate change has been widely considered by researchers at home and abroad. A large number of studies have shown that climate change will lead to the replacement of vegetation types and the migration of plant distribution ranges at altitude and latitude. When simulating the potential suitable distribution area of Alnus cremastogyne Burkill. (First published in J. Linn. Soc., Bot. 26: 499 (1899)) under climate change scenarios, Liu et al. [11] found that the highly suitable area and the total suitable area of A. cremastogyne increased, and the suitable area migrated to the northwest. Peringer et al. [12] simulated the high-altitude pastures in the central Alps of F Gettal, Valais, Switzerland. The results showed that in the very long term, the prediction of the migration of vegetation belts to higher altitudes due to climate warming is reasonable. Wang et al. [13] assessed species distribution in an alpine tundra area within the Changbai Mountains of China, and found that herb encroachment has significantly altered tundra vegetation over the past three decades, especially in the mid-low alpine tundra area of the Changbai Mountains of China. Lv and Zhou [14] found that in the past 50 years, the total climatic suitable area of the potential geographical distribution of Stipa breviflora Griseb. (First published in Nachr. Königl. Ges. Wiss. Georg-Augusts-Univ. 3: 82 (1868)) showed a significant increase and northward expansion trend, with great potential for expansion. Ramachandran et al. [15] simulated the habitat suitability of dominant tree species in the forest ecosystem of Tamil Nadu, India, and found that the suitable habitat area of evergreen and deciduous tree species may decrease, while the suitable habitat area of thorn forest species may increase. These studies reveal the profound impact of climate change on the geographical distribution of plants and provide an important basis for ecological protection and resource management.
For the study of climate change in the watershed of the Yellow River, most of the existing work is mainly based on historical data, and the research on the spatial and temporal distribution pattern of temperature and precipitation based on future emission scenarios is still scarce. In addition, most of the projections of future climate variables in the watershed of the Yellow River are directly dependent on global climate models. This method has several limitations, including the low resolution of the climate model, which makes it difficult to accurately reflect the regional climate characteristics of the Yellow River source area, and it cannot be directly applied to regional hydrological analysis. At present, there are relatively few studies on the future distribution of species in the watershed of the Yellow River, especially in the context of global warming. On the one hand, studies on the future distribution of species often use unselected climate models as input variables, and do not consider the applicability of the CMIP6 climate model data in the watershed of the Yellow River. On the other hand, due to the differences in the algorithms of different niche models, the effects and results obtained may be different when simulating the species distribution in the watershed of the Yellow River. These factors limit the in-depth understanding of ecological changes and species adaptability in the watershed of the Yellow River. Based on 39 precipitation climate models and 21 temperature climate models in the sixth International Coupled Model Intercomparison Project (CMIP6), the current study relies on the Delta statistical downscaling technique of five interpolation methods and four evaluation indexes to generate an equal weight set of 30 arcsecond preferred climate models. Based on the four niche models of MaxEnt, Garp, Bioclim, Domain, centroid migration, area transfer matrix, Mess and MoD analysis and other spatio-temporal analysis methods, the future migration patterns of S. oritrepha and P. crassifolia in the watershed of the Yellow River under different emission scenarios ssp126, ssp245, ssp370 and ssp585 in the future (2022–2050, 2051–2070) were explored.

2. Materials and Methods

2.1. Study Area

The source area of the Yellow River is located in the northwest of the Yellow River Basin, with a geographical location between 32°10′–36°05′ N and 95°50′–103°30′ E (Figure 1). The length of the main tributary of the Yellow River in the Yellow River source area is about 1552 km. The total Yellow River source area is 1.22 × 105 km2, accounting for 16.2% of the Yellow River Basin area [16]. The region has significant geographical features and a wide range of elevations, between 1600 and 3800 m, with rich biodiversity and unique ecosystems. The climate type of the Yellow River source area is plateau climate, which is characterized by high altitude, low temperature and obvious dry and wet seasonal changes. The annual average temperature is about −1 to 8 °C, and the annual precipitation is usually between 300 and 800 mm. The seasonal distribution of precipitation shows obvious single peak characteristics. The main vegetation types in the area include S. oritrepha and P. crassifolia, which not only play an important role in the ecosystem, but also provide a key habitat for local biodiversity. With its strong adaptability and ecological restoration ability, S. oritrepha has a significant contribution in improving soil quality and maintaining soil and water. As a unique tree species, P. crassifolia has become an important forest resource in the region with its good drought resistance and growth rate.

2.2. Data Acquisition

2.2.1. Species Occurrence Data

The occurrence records of S. oritrepha and P. crassifolia were derived from GBIF (https://www.gbif.org) (Accessed on 12 September 2024), and Chinese Plant Image Database (https://www.plantplus.cn/cn (Accessed on 13 September 2024). By consulting the occurrence records of existing research, 180 and 119 occurrence records were obtained, respectively. In this study, invalid and repetitive records were identified and eliminated. The dilution points were sampled by spatial filtering to reduce the influence of sampling bias on model output [17]. Finally, 47 and 26 distribution points were obtained from S. oritrepha and P. crassifolia, respectively (Tables S2 and S3).

2.2.2. Meteorological Data

The meteorological data required for this study include the following: (1) The measured data of a total of 10 stations in the watershed of the Yellow River from 1995 to 2014 are mainly from the National Meteorological Science Data Center (http://data.cma.cn/) (Accessed on 14 August 2024), including daily precipitation (pr), temperature (t), maximum temperature (tmax) and minimum temperature (tmin); (2) The 30-year average data of pr, t, tmax and tmin in China from 1971 to 2000 were used as input data to participate in Delta downscaling modeling. The spatial resolution was 30 arcseconds, which was derived from the National Science and Technology Resource Sharing Service Platform-National Ecological Science Data Center (http://www.nesdc.org.cn/) (Accessed on 17 August 2024); (3) The climate variable data required for ENMs under the current scenario (1970–2000) used in this study are from the World Climate Database (http://www.worldclim.org/) (Accessed on 18 August 2024), to obtain 19 bioclimatic variables that have an important impact on species distribution under the current climate scenario, with a resolution of 30 arcseconds (Table S1).

2.2.3. CMIP6 Climate Mode Data

For CMIP6 data (https://esgf-node.llnl.gov/search/cmip6/) (Accessed on 21 August 2024), 39-month average pr, 39-month average t, 20 tmax and 21 tmin climate model data in GCMs from 1970 to 2014 and 2022 to 2070 were selected in this study. Different forcing scenarios in the future were selected from the social sharing economic path of CMIP6, ssp1-2.6 (ssp126), ssp2-4.5 (ssp245), ssp3-7.0 (ssp370) and ssp5-8.5 (ssp585) in the typical concentration path combination scenario. In addition, the future emission scenario period of CMIP6 itself is 2015–2100, while the period selected in this study is 2022–2070.

2.2.4. Other Data

The topographic data altitude (Alt), slope (Slp) and aspect (Ast) required for the species distribution model were extracted from the digital elevation model (DEM) (http://www.gscloud.cn/) (Accessed on 19 August 2024), in the watershed of the Yellow River with a spatial resolution of 30 arcseconds.

2.3. Data Processing

Based on the Delta statistical downscaling technique, this study used the observation data of 10 surface meteorological stations in the watershed of the Yellow River from 1995 to 2014 as a reference. Bilinear Interpolation (BI), Inverse Distance Weighted (IDW), Kriging, Natural Neighbor Interpolation (NNI), Spline and other interpolation methods are used to transform CMIP6 model data with coarse-scale and low-spatial resolution into regional information with fine-scale and high-spatial resolution. Then, the Mean Absolute Error (MAE), the evaluation index based on the Taylor diagram (S), the Time Skills Score (TS) and the Spatial Skills Score (SS) were used to evaluate the accuracy of the monthly historical precipitation, monthly average temperature, maximum temperature and minimum temperature of the downscaling simulation. The optimal interpolation method was selected and the multi-model optimization set was carried out. The 30 arcsecond regional climate data for the future period (2022–2070) under four emission scenarios were generated (Figure 2).

2.4. Niche Model Optimization

In this study, four niche models, MaxEnt, Garp, Bioclim and Domain, were used to evaluate and optimize the four niche models based on historical data. The distribution range and influencing factors of S. oritrepha and P. crassifolia in the watershed of the Yellow River under future climate models were evaluated by using the optimized model (Figure 2).
MaxEnt (maximum entropy model) is a probability model based on the relationship between species distribution points and environmental variables. MaxEnt v3.4.1 software was used for modeling, and K-Fold Cross Validation (K-CV) was used for a total of 10 runs, so that each sub-sample could be used for model training and testing. The occurrence records of S. oritrepha and P. crassifolia were divided into 4 equal parts, of which 3 were used for model training and 1 was used for model testing. Appropriate feature types (such as linear, quadratic and product) were selected and regularization parameters were adjusted to optimize the prediction performance of the model. The accuracy of the model was evaluated by AUC value [18,19].
Garp (Genetic Algorithm-based Reconstruction Model) combines biological information and environmental factors to simulate species distribution. Using Garp v.1.1.3 software, the potential distribution map was generated based on the training data, and the model parameters were optimized through multiple iterations. The model results were evaluated by bio-suitability score to determine the suitable area [20,21].
The Bioclim model is based on statistical data on climate variables and is analyzed using the dismo package in the R 4.2.0. The model generates a bioclimatic model of the plant suitable area based on historical climate data, and uses the existing distribution data for correction and verification to ensure the reliability of the model results [22,23].
The Domain model predicts the suitable distribution area by defining the climatic space of the species. The ENMeval package in R language was used to construct the model, and the appropriate climate variable threshold was set to evaluate the suitable range of species under specific climatic conditions. The model was evaluated by cross-validation method to improve the credibility of the model [24].
In this study, the AUC value was used as the main evaluation index, and Kappa was used as an auxiliary to judge the consistency of the model. The output of the two evaluation indexes can be realized in DIVA-GIS, so as to optimize the niche model. From the comparison results, the MaxEnt model has the best prediction effect, and the Garp model has a worse simulation effect than the other three models (Table 1 and Table 2). Based on the natural discontinuity method, the potential distribution of the two vegetations in the watershed of the Yellow River was divided into different levels of suitable areas, which were mainly divided into three levels of suitable areas, namely unsuitable areas (0–0.32), suitable areas (0.32–0.65) and highly suitable areas (0.65–1). Because the Domain output result is the classification confidence of the species, the results of the model output are adjusted according to the previous studies and the actual distribution of the two vegetations to divide the suitable areas [25,26].

2.5. The Similarity Between Future Climate Scenarios and Current Climate

Multivariate environmental similarity surface (Mess) and the most dissimilar variable (MoD) are key indicators for assessing future climate and current climate differences. Multivariate similarity (S) is an important parameter to quantify the similarity between the climate at a certain point in the future and the current climate. When S is greater than 0, a higher value indicates that the difference between the future climate and the current climate at that location is smaller; when S equals 100, the climatic conditions between the two are exactly the same. Conversely, if S is less than 0, it implies that at least one climate variable at that location will exceed the range of the current climate in the future. Such a point is called a climate anomaly point, and the greater the negative value of S, the more significant the difference. Among these outliers, the climate variables with the lowest S value or the highest degree of anomaly, i.e., the most dissimilar variables, are likely to be the dominant factors affecting the future geographical distribution of vegetation [27,28]. Mess and MoD analysis mainly used the “density.tools.Novel” module of the MaxEnt model. In this study, based on the climate variables of the current suitable areas of S. oritrepha and P. crassifolia, the Mess and MoD methods were used to evaluate the similarity between different climate scenarios in the future and the current climate, so as to identify the climate anomaly areas and key abnormal climate factors in the suitable areas of S. oritrepha and P. crassifolia under the background of climate change.

3. Results

3.1. Optimal Selection of Ecological Niche Models

3.1.1. Screening Results of Correlation Analysis of Environmental Variables

Using the Pearson correlation test, only one of the variables with a correlation greater than 0.8 was retained. Finally, seven biological variables such as Bio_1, Bio_2, Bio_3, Bio_7, Bio_12, Bio_14 and Bio_15 were selected, and elevation, slope and aspect variables were added as terrain factors for the prediction of niche models (Figure 3).

3.1.2. Evaluation of Four Ecological Niche Models for S. oritrepha

The Area Under Curve (AUC) and Kappa values of MaxEnt, Garp, Bioclim and Domain models for simulating the potential suitable areas of S. oritrepha under the current scenario are shown in Table 1. The mean AUC values of different models were ranked as follows: MaxEnt (0.909) > Domain (0.890) > Bioclim (0.880) > Garp (0.838), and the standard deviation of AUC was ranked as follows: Garp (0.005) > Bioclim (0.007) = Domain (0.007) > MaxEnt (0.009). The mean value of Kappa was ranked as follows: MaxEnt (0.806) > Domain (0.782) > Bioclim (0.756) > Garp (0.701), and the standard deviation of Kappa was ranked as Domain (0.006) > Bioclim (0.007) > MaxEnt (0.012) > Garp (0.013). Based on the comprehensive results, the average AUC values of the four models exceeded 0.8, indicating that the effects of each model on simulating the potential distribution area of S. oritrepha in the watershed of the Yellow River were relatively good. Among them, the MaxEnt model had the best prediction effect, and the Garp model had a worse simulation effect than the other three models.

3.1.3. Evaluation of Four Ecological Niche Models for P. crassifolia

The AUC and Kappa values of the potential suitable areas of P. crassifolia simulated by MaxEnt, Garp, Bioclim and Domain models are shown in Table 2. The mean AUC of different models was ranked as follows: MaxEnt (0.909) > Domain (0.899) > Bioclim (0.886) > Garp (0.846), and the standard deviation of AUC was ranked as follows: MaxEnt (0.005) = Bioclim (0.005) > Domain (0.006) > Garp (0.007). The mean value of Kappa was ranked as follows: MaxEnt (0.818) > Domain (0.797) > Bioclim (0.789) > Garp (0.699), and the standard deviation of Kappa was ranked as Domain (0.004) > Bioclim (0.005) = MaxEnt (0.005) > Garp (0.006). The results showed that the average AUC values of the four models were more than 0.8, indicating that the effects of each model on simulating the potential distribution area of P. crassifolia in the watershed of the Yellow River were better. Among them, the MaxEnt model had the best prediction effect, and the Garp model had worse simulation effect than the other three models.
By comparing and evaluating the performance of S. oritrepha and P. crassifolia predicted by the four niche models in simulating the potential suitable areas in the watershed of the Yellow River under the current climate scenario, it was found that the MaxEnt model had better simulation results. Therefore, the prediction results of the MaxEnt model were selected as the final results for analysis, and the MaxEnt model was used as the best model for future suitable area prediction.

3.2. Distribution Range of Potential Suitable Areas for S. oritrepha Under Future Climate Scenarios

3.2.1. Distribution Range of Potential Suitable Habitats

By 2050, under different scenarios (ssp126, ssp245, ssp370, ssp585), the spatial distribution pattern of S. oritrepha was basically consistent. The potential distribution probability of suitable areas showed an increasing trend from east to west, and the highly suitable areas were concentrated in the eastern part of the Yellow River source area. Compared with the current period, the highly suitable areas and overall suitable areas increased substantially, while the unsuitable areas decreased sharply, showing a fragmented distribution only in the northwest corner (Figure 4, Figure 5 and Figure 6). By 2070, the spatial distribution of each scenario is substantially different. Under the ssp126 scenario, most of the Yellow River source area is a highly suitable area for S. oritrepha. Compared with 2050, a large area of highly suitable areas also appears in the western part of the Yellow River source area. The distribution of unsuitable areas and suitable areas decreases significantly, and they are scattered in the Yellow River source area in a fragmented manner. The distribution of potential suitable areas of ssp245, ssp370 and ssp585 scenarios is similar. The highly suitable areas are still concentrated in the eastern part of the Yellow River source area. Unlike ssp126, there is no large-scale highly suitable area in the west. In addition, the suitable area under each scenario is ssp126 > ssp370 > ssp585 > ssp245.
From the current period to 2050, the suitable area of S. oritrepha under different scenarios increased by 7.04% (ssp126), 11.15% (ssp245), 11.64% (ssp370) and 12.55% (ssp585), respectively. The highly suitable areas increased more significantly, by 107.10%, 115.69%, 118.51% and 134.14%, respectively, which was more than twice the current period. By 2070, except for the shrinkage of the suitable and highly suitable areas under the ssp245 scenario, the highly suitable areas in the remaining scenarios continued to expand, with an increase of 216.81% (ssp126), 156.82% (ssp370) and 135.12% (ssp585), respectively. The unsuitable areas decreased by more than 50% under each scenario. Under the ssp126, ssp370 and ssp585 scenarios, the unsuitable area of the watershed of the Yellow River was only about 2.47 × 104 km2, 2.54 × 104 km2 and 2.42 × 104 km2. Overall, the suitable and highly suitable areas under each scenario increased substantially from the current period to 2070 (Table 3).

3.2.2. The Change in Spatial Pattern of Suitable Area

From 2022 to 2050, the centroids under the ssp126, ssp245, ssp370 and ssp585 scenarios move northwestward by 31.91 km, 44.50 km, 41.46 km and 60.43 km, respectively. The migration distance under the ssp585 scenario is the largest. By 2070, the centroid migration direction is different under each scenario: ssp126 and ssp585 migrate to the southeast, which are 27.91 km and 33.11 km, respectively. Under the ssp245 scenario, the westward migration is 40.66 km; under the ssp370 scenario, the northwestward migration is 26.43 km. In general, from the current period to 2070, the centroid of the suitable area of S. oritrepha generally showed a trend of moving to the northwest and high-altitude areas (Figure 7).
Under the ssp126 scenario, the watershed of the Yellow River is almost all an expansion area, indicating that the suitability degree is transformed from low to high, while other regions are stable areas with unchanged suitability degree, and a small number of shrinkage areas are located in the middle of the watershed of the Yellow River. Under the ssp245 scenario, the expansion area is concentrated in the western and some eastern regions of the Yellow River watershed, with a contraction area in the central region and a stable area in most regions. The ssp370 and ssp585 scenarios show a stable area–expansion area pattern of east–west distribution, and the contraction area is limited to the middle. In general, the ssp126 and ssp245 scenarios show a significant expansion trend, while the ssp370 and ssp585 scenarios are dominated by stable areas (Figure 8).

3.3. Distribution Range of Potential Suitable Areas for P. crassifolia Under Future Climate Scenarios

3.3.1. Distribution Range of Potential Suitable Habitats

By 2050, the distribution pattern of P. crassifolia under different scenarios (ssp126, ssp245, ssp370, ssp585) was relatively consistent. The unsuitable area, suitable area and highly suitable area showed obvious east–west distribution, and the suitable area increased significantly compared with the current period. The suitable area and the highly suitable area were distributed in the east of the Yellow River source area (Figure 9, Figure 10 and Figure 11). By 2070, the distribution pattern under each scenario was similar to that in 2050, showing the characteristics of east–west distribution and eastern fragmentation distribution. Except for the ssp245 scenario, the suitable and highly suitable areas increased. In general, compared with the current period, the suitable area of P. crassifolia increased significantly in 2070, and the suitable and highly suitable areas in the eastern and northern parts of the Yellow River source area were still mainly fragmented.
From the current period to 2050, the suitable area of P. crassifolia under different scenarios (ssp126, ssp245, ssp370, ssp585) increased by 80.99%, 90.51%, 80.94% and 83.60%, respectively. The highly suitable area increased more substantially, reaching 411.76%, 498.32%, 448.59% and 502.41%, respectively, which was more than four times the current period, and the suitable area accounted for about 30% of the total Yellow River source area under each scenario. By 2070, except for the shrinkage of the suitable area under the ssp245 scenario, the expansion of the highly suitable area under other scenarios was more obvious, increasing by 643.91% (ssp126), 721.34% (ssp370) and 806.40% (ssp585), respectively, and the unsuitable area remained at about 8 × 104 km2. In general, the suitable and highly suitable areas increased substantially from the current period to 2070 (Table 4).

3.3.2. The Change in Spatial Pattern of Suitable Area

With the migration of the P. crassifolia suitable area, its centroid position also changed significantly. From 2022 to 2050, the centroids of ssp126, ssp245, ssp370 and ssp585 moved westward by 37.07 km, 41.63 km, 49.28 km and 44.34 km, respectively. By 2070, the mass center migration direction was different under different scenarios: ssp126 and ssp370 scenarios migrated to the southeast by 17.54 km and 23.81 km, respectively; the ssp245 migrated 69.22 km to the northwest. The ssp585 migrated 19.70 km to the southwest. In general, from the current period to 2070, the centroid of the suitable area for P. crassifolia moves generally westward and to higher altitude areas (Figure 12).
Under the scenarios of ssp126, ssp245 and ssp370, the distribution pattern of the stable area and the expansion area in the watershed of the Yellow River is similar, showing obvious east–west distribution characteristics. In the future, there will be more areas with lower suitability in the eastern region to be transformed into areas with higher suitability, and the rest will remain stable, with almost no shrinkage areas. However, the ssp245 scenario is different from other scenarios. There are more shrinkage areas in the east, indicating that the suitability of the region may decrease in the future, while there are only a few expansion areas in the north and south. In general, ssp126, ssp370 and ssp585 showed a significant expansion trend, while ssp245 was mainly in the stable area, with a small amount of contraction area (Figure 13).

3.4. Differences in Climate Variables

By 2050, the mean values of multivariate similarity under the scenarios of ssp126, ssp245, ssp370 and ssp585 were 7.69, 7.87, 7.96 and 7.85, respectively. The proportion of negative values (climate anomaly areas) in the watershed of the Yellow River was 13.72%, 12.83%, 13.46% and 11.84%, respectively, mainly distributed in the south and north of the watershed. The most dissimilar variables were mean annual temperature (Bio_1), mean annual precipitation (Bio_12), driest month precipitation (Bio_14) and precipitation coefficient of variation (Bio_15). By 2070, the mean values of multivariate similarity under the four scenarios decreased to 6.78, 4.45, 6.89 and 6.64, respectively, and the proportion of negative regions increased to 33.57%, 24.78%, 20.49% and 24.61%, respectively. These areas were also mainly distributed in the south and north of the Yellow River source area, and there was also a certain distribution in the west of the source area under the ssp245 scenario. The most dissimilar variables remained unchanged, including annual average temperature (Bio_1), annual average precipitation (Bio_12), precipitation in the driest month (Bio_14) and precipitation coefficient of variation (Bio_15) (Figure 14 and Figure 15).

4. Discussions

By 2050 and 2070, the suitable area and highly suitable area of the watershed of the Yellow River have expanded significantly, and the distribution centroid moved westward or northwestward (i.e., high-altitude and high-latitude area). This trend indicates that more current unsuitable areas will be transformed into suitable areas in high-altitude areas in the future, which is consistent with the research results of Ma et al. and Vento et al. [29,30], indicating that many mountain plant species will migrate to higher altitudes to cope with global climate change [31,32].
For S. oritrepha, its potential distribution was affected by meteorological factors such as the annual average temperature difference between day and night (Bio_2), the coefficient of variation in precipitation (Bio_15), the annual average precipitation (Bio_12) and the annual average temperature (Bio_1). Among them, the annual average temperature was the most important, indicating that the factor played a leading role in the growth of S. oritrepha. Previous studies have pointed out that with the intensification of drought stress caused by climate warming, the radial growth of S. oritrepha shrubs along the elevation gradient up to the shrub line may change from temperature limitation to humidity limitation [6]. Based on the response of tree-ring width to climatic factors, the growth of polar willow and birch was limited by the early temperature of the growing season, while the evergreen shrub juniper was mainly regulated by regional temperature from June to July [33]. Weijers et al. [34] also found that temperature, especially summer temperature, has a significant effect on vegetation growth. Thakur et al. [35] used Rhododendron anthopogon Wall. (First published in Mem. Wern. Nat. Hist. Soc. 3: 409 (1821)), a Himalayan alpine shrub, as the research subject, indicating that dwarf shrubs benefit from increased temperature, resulting in increased shrubization of alpine ecosystems. Therefore, the increase in annual average temperature in the future will promote the growth of S. oritrepha in high-altitude areas.
For P. crassifolia, mean annual temperature (Bio_1) and mean annual precipitation (Bio_12) are equally important. Different from S. oritrepha, the suitable area of P.crassifolia expanded from the current period (1970–2000) to 2050, while the suitable area remained basically unchanged from 2022 to 2070, and even decreased under the ssp245 scenario, indicating that the response of trees to climatic factors was different from that of shrubs. Previous studies have shown that temperature is a key climatic factor for the growth of trees in alpine forests, especially affecting nutrient cycling and photosynthetic characteristics [36]. The studies of Kummel et al. and Qin et al. also confirmed the importance of growing season temperature to tree growth [37,38]. The results showed that the increase in temperature may be the reason for the expansion of the suitable area of P. crassifolia between 1970 and 2070. The study also found that from 2012 to 2017, the radial growth of P. crassifolia was significantly negatively correlated with temperature and significantly positively correlated with precipitation [39]. Hallinger et al. [40] pointed out that high temperature may indirectly limit tree growth by accelerating transpiration, and insufficient precipitation will reduce relative humidity and available water in the forest. Therefore, high temperature and drought stress during 2022–2070 may be the main reason for the limited increase in the suitable area of P. crassifolia. In addition, studies by Gao et al. and Hu et al. have found that the suitable area of P. crassifolia in the Qinghai–Tibet Plateau may shrink in the future [41,42].
With climate change, the temperature rise in the watershed of the Yellow River will be conducive to the survival of vegetation, but by 2070, water stress may lead to the shrinkage of the suitable area for some species. This has important guiding significance for understanding the future hydrological cycle and ecological protection planning in the watershed of the Yellow River. At present, the arbor and shrub vegetation in the source area of the Yellow River is mainly concentrated in the east and south. It is suggested to increase the area of nature reserves in the northwest to adapt to the future migration trend of vegetation to high-altitude areas. Finally, it should be noted that this study only considers climatic factors and topographic factors, and does not consider human factors (such as afforestation, over-reclamation and grazing), so the simulation results may be different from the actual distribution.

5. Conclusions

Taking the watershed of the Yellow River as the research area, the Delta downscaling model based on the multi-interpolation method converts the coarse-scale and low-resolution CMIP6 model data into fine-scale and high-resolution regional ground information, and then forms the pr, t, tmax and tmin data sets suitable for the watershed of the Yellow River under the future multi-emission scenario. Combined with the MaxEnt model, the migration of the future suitable area of typical arbor and shrub vegetation in the watershed of the Yellow River was predicted. At present, the suitable areas of S. oritrepha and P. crassifolia are mainly concentrated in the high-altitude areas of the source area of the Yellow River. With climate change, the suitable areas are expected to increase significantly in 2050 and 2070, especially in the ssp126 scenario, the number and quality of suitable areas have improved. Under different climate scenarios, the expansion trend of S. oritrepha ’s suitable area is obvious, especially under mild climate change scenarios (such as ssp126), showing an enhanced ability to adapt to the future. Relatively speaking, the suitable area of P. crassifolia changed little, showing its tolerance to environmental changes. This study reveals the potential impact of climate change on plant distribution, but also points out the limitations of environmental variable selection and the shortcomings of the single use of future climate models. Subsequent studies combined more environmental factors and diverse climate models to comprehensively assess the impact of climate change on niche.

Supplementary Materials

The following supporting information can be downloaded at: https://www.mdpi.com/article/10.3390/f16030448/s1, Table S1: 19 Bioclimatic variables; Table S2: Geographical location information of Salix oritrepha Schneid; Table S3: Geographical location information of Picea crassifolia Kom.

Author Contributions

Conceptualization, S.D.; Data curation, S.J.; Funding acquisition, S.D.; Methodology, S.J.; Resources, S.J. and S.D.; Software, L.K. and Y.H.; Supervision, J.W.; Validation, Y.H. and J.W.; Writing—original draft, L.K.; Writing—review and editing, L.K. All authors have read and agreed to the published version of the manuscript.

Funding

This research was funded by the Science fund for distinguished young scholars of Henan Province (232300421017) (Dou Shentang); the National Key Research Priorities Program of China (2023YFC3209303) (Jian Shengqi); Qian Kehe Zhicheng [2023] Yiban 206 (Jian Shengqi); Qian Kehe Zhicheng [2024] Yiban 130 (Jian Shengqi).

Data Availability Statement

The data that support the findings of this study are available from the corresponding author upon reasonable request.

Conflicts of Interest

The authors declare no conflicts of interest.

References

  1. Nooni, I.K.; Hagan, D.F.T.; Wang, G.; Ullah, W.; Lu, J.; Li, S.; Dzakpasu, M.; Prempeh, N.A.; Lim Kam Sian, K.T. Future Changes in Simulated Evapotranspiration across Continental Africa Based on CMIP6 CNRM-CM6. Int. J. Environ. Res. Public Health 2021, 18, 6760. [Google Scholar] [CrossRef]
  2. Zhang, X.; Yang, Y.; Gao, H.; Xu, S.; Feng, J.; Qin, T. Land Cover Changes and Driving Factors in the Source Regions of the Yangtze and Yellow Rivers over the Past 40 Years. Land 2024, 13, 259. [Google Scholar] [CrossRef]
  3. Liu, J.; Yi, Z.; Fang, Y.; Wu, C. Ecological Vulnerability Assessment of the Three Rivers Source Area Based on the Analytic Hierarchy Process. Water 2024, 16, 2023. [Google Scholar] [CrossRef]
  4. Chou, L.; Jia, W.; Zhang, Y.; Lan, X.; Luo, H.; Yu, Z.; Wang, Z. Water Niche Characteristics of Subalpine Shrub in Eastern Qilian Mountains, China. Ecohydrology 2024, 17, 2726. [Google Scholar] [CrossRef]
  5. Wang, Y.; Liang, E.; Camarero, J.J. Encroachment drives facilitation at alpine shrublines. For. Ecosyst. 2024, 11, 100168. [Google Scholar] [CrossRef]
  6. Han, Y.; Wang, Y.; Liu, B.; Huang, R.; Julio Camarero, J. Moisture mediates temperature-growth couplings of high-elevation shrubs in the Tibetan plateau. Trees-Struct. Funct. 2022, 36, 273–281. [Google Scholar] [CrossRef]
  7. Lu, X.; Hu, F.; Liang, E.; Sigdel, S.R.; Shang, Z.; Camarero, J.J. Loss of growth resilience towards the alpine shrubline. For. Ecol. Manag. 2023, 539, 121013. [Google Scholar] [CrossRef]
  8. Fang, S.; He, Z. Fifty Years of Change in a Coniferous Forest in the Qilian Mountains, China-Advantages of High-Definition Remote Sensing. Forests 2020, 11, 1188. [Google Scholar] [CrossRef]
  9. Rong, Z.; Zhao, C.; Liu, J.; Gao, Y.; Zang, F.; Guo, Z.; Mao, Y.; Wang, L. Modeling the Effect of Climate Change on the Potential Distribution of Qinghai Spruce (Picea crassifolia Kom.) in Qilian Mountains. Forests 2019, 10, 62. [Google Scholar] [CrossRef]
  10. Peng, Z.; Mo, Q.; Zhu, L.; Lu, Q.; Cai, J.; Guo, M.; Xu, K.; Zhang, Y. Divergent responses of Picea crassifolia Kom. in different forest patches to climate change in the northeastern Tibetan Plateau. For. Ecosyst. 2023, 10, 100153. [Google Scholar] [CrossRef]
  11. Liu, L.; Wang, R.; Zhang, Y.; Mou, Q.; Gou, Y.; Liu, K.; Huang, N.; Ouyang, C.; Hu, J.; Du, B. Simulation of potential suitable distribution of Alnus cremastogyne Burk. In China under climate change scenarios. Ecol. Indic. 2021, 133, 108396. [Google Scholar] [CrossRef]
  12. Peringer, A.; Frank, V.; Snell, R.S. Climate change simulations in Alpine summer pastures suggest a disruption of current vegetation zonation. Glob. Ecol. Conserv. 2022, 37, e02140. [Google Scholar] [CrossRef]
  13. Wang, L.; Wang, W.J.; Wu, Z.; Du, H.; Zong, S.; Ma, S. Potential Distribution Shifts of Plant Species under Climate Change in Changbai Mountains, China. Forests 2019, 10, 498. [Google Scholar] [CrossRef]
  14. Lv, X.; Zhou, G. Climatic Suitability of the Geographic Distribution of Stipa breviflora in Chinese Temperate Grassland under Climate Change. Sustainability 2018, 10, 3767. [Google Scholar] [CrossRef]
  15. Ramachandran, A.; Manickavasagam, M.; Hariharan, S.; Mathan, M.; Ahamed Ibrahim, S.N.; Kumar, D.S.; Joseph, K. Assessment of species migration patterns in forest ecosystems of Tamil Nadu, India, under changing climate scenarios. Theor. Appl. Climatol. 2024, 155, 9185–9196. [Google Scholar] [CrossRef]
  16. Guo, B.; Wei, C.; Yu, Y.; Liu, Y.; Li, J.; Meng, C.; Cai, Y. The dominant influencing factors of desertification changes in the source region of Yellow River: Climate change or human activity? Sci. Total Environ. 2022, 813, 152512. [Google Scholar] [CrossRef] [PubMed]
  17. Zhao, Y.; O’Neill, G.A.; Coops, N.C.; Wang, T. Predicting the site productivity of forest tree species using climate niche models. For. Ecol. Manag. 2024, 562, 121936. [Google Scholar] [CrossRef]
  18. Munna, A.H.; Amuri, N.A.; Hieronimo, P.; Woiso, D.A. Modelling ecological niches of Sclerocarya birrea subspecies in Tanzania under the current and future climates. Silva Fenn. 2023, 57, 23009. [Google Scholar] [CrossRef]
  19. Zhao, R.; Wang, S.; Chen, S. Predicting the potential habitat suitability of Saussurea species in China under future climate scenarios using the optimized Maximum Entropy (MaxEnt) model. J. Clean. Prod. 2024, 474, 143552. [Google Scholar] [CrossRef]
  20. Buebos-Esteve, D.E.; Mamasig, G.D.N.S.; Ringor, A.M.D.; Layog, H.N.B.; Murillo, L.C.S.; Dagamac, N.H.A. Modeling the potential distribution of two immortality flora in the Philippines: Applying MaxEnt and GARP algorithms under different climate change scenarios. Model. Earth Syst. Environ. 2023, 9, 2857–2876. [Google Scholar] [CrossRef]
  21. Jian, S.-q.; Zhu, T.-s.; Hu, C.-h. Integrating potential distribution of dominant vegetation and land use into ecological restoration in the Yellow River Basin, China. J. Mt. Sci. 2022, 19, 2886–2904. [Google Scholar] [CrossRef]
  22. Wang, X.; Jiang, Y.; Wu, W.; He, X.; Wang, Z.; Guan, Y.; Xu, N.; Chen, Q.; Shen, Y.; Cao, J. Cryptosporidiosis threat under climate change in China: Prediction and validation of habitat suitability and outbreak risk for human-derived Cryptosporidium based on ecological niche models. Infect. Dis. Poverty 2023, 12, 72–86. [Google Scholar] [CrossRef]
  23. Xie, C.; Chen, L.; Li, M.; Jim, C.Y.; Liu, D. BIOCLIM Modeling for Predicting Suitable Habitat for Endangered Tree Tapiscia sinensis (Tapisciaceae) in China. Forests 2023, 14, 2275. [Google Scholar] [CrossRef]
  24. Carpenter, G.; Gillison, A.N.; Winter, J. Domain- A Flexible Modeling Procedure For Mapping Potential Distributions Of Plants And Animals. Biodivers. Conserv. 1993, 2, 667–680. [Google Scholar] [CrossRef]
  25. Xie, C.P.; Chen, L.; Li, M.; Liu, D.W.; Jim, C.Y. Spatial-Temporal Distribution Pattern of Ormosia hosiei in Sichuan under Different Climate Scenarios. Forests 2023, 14, 1261. [Google Scholar] [CrossRef]
  26. Shi, X.; Yin, Q.; Sang, Z.; Zhu, Z.; Jia, Z.; Ma, L. Habitat Distribution Pattern of Rare and Endangered Plant Magnolia wufengensis in China under Climate Change. Forests 2023, 14, 1767. [Google Scholar] [CrossRef]
  27. Feng, C.; Guo, F.; Gao, G. Climate as a Predictive Factor for Invasion: Unravelling the Range Dynamics of Carpomya vesuviana Costa. Insects 2024, 15, 374. [Google Scholar] [CrossRef] [PubMed]
  28. Gilbert, N.A.; Kolbe, S.R.; Eyster, H.N.; Grinde, A.R. Can internal range structure predict range shifts? J. Anim. Ecol. 2024, 93, 1556–1566. [Google Scholar] [CrossRef]
  29. Ma, Q.; Li, Y.; Li, X.; Liu, J.; Keyimu, M.; Zeng, F.; Liu, Y. Modeling future changes in potential habitats of five alpine vegetation types on the Tibetan Plateau by incorporating snow depth and snow phenology. Sci. Total Environ. 2024, 918, 170399. [Google Scholar] [CrossRef]
  30. Vento, B.; Rivera, J.; Ontivero, M. Climate influence on future suitability of high-altitude wetlands in two natural protected areas from the Central Andes of Argentina. Perspect. Ecol. Conserv. 2024, 22, 240–249. [Google Scholar] [CrossRef]
  31. Lesica, P. Arctic-Alpine Plants Decline over Two Decades in Glacier National Park, Montana, U.S.A. Arct. Antarct. Alp. Res. 2018, 46, 327–332. [Google Scholar] [CrossRef]
  32. Dong, P.B.; Wang, L.Y.; Wang, L.J.; Jia, Y.; Li, Z.H.; Bai, G.; Zhao, R.M.; Liang, W.; Wang, H.Y.; Guo, F.X.; et al. Distributional Response of the Rare and Endangered Tree Species Abies chensiensis to Climate Change in East Asia. Biology 2022, 11, 1659. [Google Scholar] [CrossRef]
  33. Blok, D.; Sass-Klaassen, U.; Schaepman-Strub, G.; Heijmans, M.M.P.D.; Sauren, P.; Berendse, F. What are the main climate drivers for shrub growth in Northeastern Siberian tundra? Biogeosciences 2011, 8, 1169–1179. [Google Scholar] [CrossRef]
  34. Weijers, S.; Pape, R.; Loeffler, J.; Myers-Smith, I.H. Contrasting shrub species respond to early summer temperatures leading to correspondence of shrub growth patterns. Environ. Res. Lett. 2018, 13, 034005. [Google Scholar] [CrossRef]
  35. Thakur, D.; Altman, J.; Jandová, V.; Fibich, P.; Münzbergová, Z.; Doležal, J. Global warming alters Himalayan alpine shrub growth dynamics and climate sensitivity. Sci. Total Environ. 2024, 916, 170252. [Google Scholar] [CrossRef] [PubMed]
  36. Du, H.; Li, M.H.; Rixen, C.; Zong, S.; Stambaugh, M.; Huang, L.; He, H.S.; Wu, Z. Sensitivity of recruitment and growth of alpine treeline birch to elevated temperature. Agric. For. Meteorol. 2021, 304–305, 108403. [Google Scholar] [CrossRef]
  37. Kummel, M.; McGarigal, A.; Kummel, M.; Earnest, C.; Feiden, M. Tree establishment and growth drive treeline advance and change treeline form on Pikes Peak (Colorado) in response to recent anthropogenic warming. Can. J. For. Res. 2021, 51, 1458–1471. [Google Scholar] [CrossRef]
  38. Qin, L.; Liu, K.; Shang, H.; Zhang, T.; Yu, S.; Zhang, R. Minimum temperature during the growing season limits the radial growth of timberline Schrenk spruce (P. schrenkiana). Agric. For. Meteorol. 2022, 322, 109004. [Google Scholar] [CrossRef]
  39. Wang, B.; Chen, T.; Li, C.J.; Xu, G.B.; Wu, G.J.; Liu, G.X. Radial growth of Qinghai spruce (Picea crassifolia Kom.) and its leading influencing climate factor varied along a moisture gradient. For. Ecol. Manag. 2020, 476, 118474. [Google Scholar] [CrossRef]
  40. Hallinger, M.; Manthey, M.; Wilmking, M. Establishing a missing link: Warm summers and winter snow cover promote shrub expansion into alpine tundra in Scandinavia. New Phytol. 2010, 186, 890–899. [Google Scholar] [CrossRef] [PubMed]
  41. Gao, Y.; He, Z.; Zhu, X.; Chen, L.; Du, J.; Lin, P.; Tian, Q.; Kong, J. Potential distribution and habitat suitability of Picea crassifolia with climate change scenarios. Can. J. For. Res. 2021, 51, 1903–1915. [Google Scholar] [CrossRef]
  42. Hu, H.; Wei, Y.; Wang, W.; Wang, C. The Influence of Climate Change on Three Dominant Alpine Species under Different Scenarios on the Qinghai–Tibetan Plateau. Diversity 2021, 13, 682. [Google Scholar] [CrossRef]
Forests 16 00448 g001
Figure 1. Location map of the source area of the Yellow River (map lines delineate study areas and do not necessarily depict accepted national boundaries).
Figure 1. Location map of the source area of the Yellow River (map lines delineate study areas and do not necessarily depict accepted national boundaries).
Forests 16 00448 g001
Forests 16 00448 g002
Figure 2. Technology roadmap.
Figure 2. Technology roadmap.
Forests 16 00448 g002
Forests 16 00448 g003
Figure 3. Matrix of correlation coefficients of bioclimatic variables.
Figure 3. Matrix of correlation coefficients of bioclimatic variables.
Forests 16 00448 g003
Forests 16 00448 g004
Figure 4. The potential distribution of S. oritrepha in the headwaters of the Yellow River predicted by the MaxEnt model under the current scenario.
Figure 4. The potential distribution of S. oritrepha in the headwaters of the Yellow River predicted by the MaxEnt model under the current scenario.
Forests 16 00448 g004
Forests 16 00448 g005
Figure 5. The potential distribution of S. oritrepha in the headwaters of the Yellow River predicted by MaxEnt’s model under different scenarios (2022–2050) in the future.
Figure 5. The potential distribution of S. oritrepha in the headwaters of the Yellow River predicted by MaxEnt’s model under different scenarios (2022–2050) in the future.
Forests 16 00448 g005
Forests 16 00448 g006
Figure 6. The potential distribution of S. oritrepha in the source area of the Yellow River predicted by the MaxEnt model under different scenarios (2051–2070) in the future.
Figure 6. The potential distribution of S. oritrepha in the source area of the Yellow River predicted by the MaxEnt model under different scenarios (2051–2070) in the future.
Forests 16 00448 g006
Forests 16 00448 g007
Figure 7. Migration of the centroid of S. oritrepha in the upper suitable zone of the Yellow River source area under different climate scenarios in the current period, 2021–2050 and 2051–2070.
Figure 7. Migration of the centroid of S. oritrepha in the upper suitable zone of the Yellow River source area under different climate scenarios in the current period, 2021–2050 and 2051–2070.
Forests 16 00448 g007
Forests 16 00448 g008
Figure 8. Stability–contraction–expansion of the suitable area of S. oritrepha in the source area of the Yellow River from the current period to 2070.
Figure 8. Stability–contraction–expansion of the suitable area of S. oritrepha in the source area of the Yellow River from the current period to 2070.
Forests 16 00448 g008
Forests 16 00448 g009
Figure 9. The potential distribution of P. crassifolia in the Yellow River source area as predicted by the MaxEnt model under the current scenario.
Figure 9. The potential distribution of P. crassifolia in the Yellow River source area as predicted by the MaxEnt model under the current scenario.
Forests 16 00448 g009
Forests 16 00448 g010
Figure 10. The potential distribution of P. crassifolia in the Yellow River source area predicted by MaxEnt model under different future scenarios (2022–2050).
Figure 10. The potential distribution of P. crassifolia in the Yellow River source area predicted by MaxEnt model under different future scenarios (2022–2050).
Forests 16 00448 g010
Forests 16 00448 g011
Figure 11. The potential distribution of P. crassifolia in the Yellow River source area predicted by MaxEnt model under different future scenarios (2051–2070).
Figure 11. The potential distribution of P. crassifolia in the Yellow River source area predicted by MaxEnt model under different future scenarios (2051–2070).
Forests 16 00448 g011
Forests 16 00448 g012
Figure 12. Migration of the centroid of P. crassifolia in the upper suitable zone of the Yellow River source area under different climate scenarios in the current period, 2021–2050 and 2051–2070.
Figure 12. Migration of the centroid of P. crassifolia in the upper suitable zone of the Yellow River source area under different climate scenarios in the current period, 2021–2050 and 2051–2070.
Forests 16 00448 g012
Forests 16 00448 g013
Figure 13. Stability–contraction–expansion of the suitable area of P. crassifolia in the source area of the Yellow River from the current period to 2070.
Figure 13. Stability–contraction–expansion of the suitable area of P. crassifolia in the source area of the Yellow River from the current period to 2070.
Forests 16 00448 g013
Forests 16 00448 g014
Figure 14. The most dissimilar variables from the current period to 2050 and 2070 in the watershed of the Yellow River. (a,c,e,g) are different scenarios from 2022 to 2050; (b,d,f,h) are different scenarios from 2051 to 2070.
Figure 14. The most dissimilar variables from the current period to 2050 and 2070 in the watershed of the Yellow River. (a,c,e,g) are different scenarios from 2022 to 2050; (b,d,f,h) are different scenarios from 2051 to 2070.
Forests 16 00448 g014
Forests 16 00448 g015
Figure 15. Multivariate similarity distribution in the watershed of the Yellow River from the current period to 2050 and 2070. (a,c,e,g) are different scenarios from 2022 to 2050; (b,d,f,h) are different scenarios from 2051 to 2070.
Figure 15. Multivariate similarity distribution in the watershed of the Yellow River from the current period to 2050 and 2070. (a,c,e,g) are different scenarios from 2022 to 2050; (b,d,f,h) are different scenarios from 2051 to 2070.
Forests 16 00448 g015
Table 1. AUC and Kappa values of four ecological niche models of S. oritrepha.
Table 1. AUC and Kappa values of four ecological niche models of S. oritrepha.
MaxEntGarpBioclimDomain
AUCKappaAUCKappaAUCKappaAUCKappa
10.9140.7940.8410.7010.8810.7530.8920.771
20.9040.8200.8340.6950.8720.7650.8970.782
30.9200.7950.8490.7270.8940.7450.9010.792
40.9160.8160.8330.6940.8730.7620.8860.779
50.9060.8110.8290.6890.8880.7550.8910.780
60.8960.7950.8370.6990.8760.7570.8790.782
70.9040.8200.8420.6970.8830.7680.8810.791
80.9030.7940.8360.7090.8750.7490.8940.785
90.9150.8150.8380.6820.8820.7520.8840.779
100.9210.7980.8380.7140.8750.7520.8920.781
average value0.9090.8060.8380.7010.8800.7560.8900.782
standard deviation0.0090.0120.0050.0130.0070.0070.0070.006
Table 2. AUC and Kappa values of four ecological niche models of P. crassifolia.
Table 2. AUC and Kappa values of four ecological niche models of P. crassifolia.
MaxEntGarpBioclimDomain
AUCKappaAUCKappaAUCKappaAUCKappa
10.9240.8140.8510.7110.8810.7830.9010.801
20.9330.8200.8540.6890.8820.7950.8970.792
30.9310.8150.8490.7070.8940.7850.9110.802
40.9230.8260.8430.6940.8870.7920.8960.799
50.9240.8110.8390.6990.8860.7850.8880.789
60.9290.8160.8370.7050.8790.7870.9040.798
70.9330.8180.8480.7010.8930.7980.8920.801
80.9340.8220.8460.6970.8830.7890.9050.795
90.9240.8150.8520.6880.8870.7920.8960.799
100.9210.8230.8440.7100.8840.7820.8990.802
average value0.9280.8180.8460.6990.8860.7890.8990.797
standard deviation0.0050.0050.0060.0080.0050.0050.0070.004
Table 3. The unsuitable areas, suitable areas and highly suitable areas of S. oritrepha under different climate scenarios in the current period, 2021–2050 and 2051–2070.
Table 3. The unsuitable areas, suitable areas and highly suitable areas of S. oritrepha under different climate scenarios in the current period, 2021–2050 and 2051–2070.
Area of Suitable Habitats at Different Levels/×104 km2
PeriodClimate modelS. oritrepha
UnsuitableSuitableHighly Suitable
Current
period		5.384.921.90
2022–2050ssp1263.005.273.93
ssp2452.645.744.09
ssp3702.565.504.14
ssp5852.225.544.44
2051–2070ssp1262.473.726.01
ssp2454.464.693.05
ssp3702.544.794.87
ssp5852.425.334.46
Table 4. The unsuitable areas, suitable areas and highly suitable areas of P. crassifolia under different climate scenarios in the current period, 2021–2050 and 2051–2070.
Table 4. The unsuitable areas, suitable areas and highly suitable areas of P. crassifolia under different climate scenarios in the current period, 2021–2050 and 2051–2070.
Area of Suitable Habitats at Different Levels/×104 km2
PeriodClimatic ModelP. crassifolia
UnsuitableSuitableHighly Suitable
Current
period		10.641.300.26
2022–2050ssp1268.522.351.33
ssp2458.172.471.56
ssp3708.422.351.43
ssp5858.252.381.57
2051–2070ssp1268.122.151.93
ssp2459.881.330.98
ssp3707.642.422.14
ssp5857.462.382.36
Disclaimer/Publisher’s Note: The statements, opinions and data contained in all publications are solely those of the individual author(s) and contributor(s) and not of MDPI and/or the editor(s). MDPI and/or the editor(s) disclaim responsibility for any injury to people or property resulting from any ideas, methods, instructions or products referred to in the content.

Share and Cite

MDPI and ACS Style

Jian, S.; Kong, L.; Dou, S.; Han, Y.; Wang, J. Study on the Distribution Range and Influencing Factors of Salix oritrepha Schneid. and Picea crassifolia Kom. in the Watershed of the Yellow River Under Future Climate Models. Forests 2025, 16, 448. https://doi.org/10.3390/f16030448

AMA Style

Jian S, Kong L, Dou S, Han Y, Wang J. Study on the Distribution Range and Influencing Factors of Salix oritrepha Schneid. and Picea crassifolia Kom. in the Watershed of the Yellow River Under Future Climate Models. Forests. 2025; 16(3):448. https://doi.org/10.3390/f16030448

Chicago/Turabian Style

Jian, Shengqi, Lilin Kong, Shentang Dou, Yufei Han, and Jiayi Wang. 2025. "Study on the Distribution Range and Influencing Factors of Salix oritrepha Schneid. and Picea crassifolia Kom. in the Watershed of the Yellow River Under Future Climate Models" Forests 16, no. 3: 448. https://doi.org/10.3390/f16030448

APA Style

Jian, S., Kong, L., Dou, S., Han, Y., & Wang, J. (2025). Study on the Distribution Range and Influencing Factors of Salix oritrepha Schneid. and Picea crassifolia Kom. in the Watershed of the Yellow River Under Future Climate Models. Forests, 16(3), 448. https://doi.org/10.3390/f16030448

Note that from the first issue of 2016, this journal uses article numbers instead of page numbers. See further details here.

Article Metrics

Back to TopTop